Automatic speaker recognition(ASR)systems are the field of Human-machine interaction and scientists have been using feature extraction and feature matching methods to analyze and synthesize these signals.One of the mo...Automatic speaker recognition(ASR)systems are the field of Human-machine interaction and scientists have been using feature extraction and feature matching methods to analyze and synthesize these signals.One of the most commonly used methods for feature extraction is Mel Frequency Cepstral Coefficients(MFCCs).Recent researches show that MFCCs are successful in processing the voice signal with high accuracies.MFCCs represents a sequence of voice signal-specific features.This experimental analysis is proposed to distinguish Turkish speakers by extracting the MFCCs from the speech recordings.Since the human perception of sound is not linear,after the filterbank step in theMFCC method,we converted the obtained log filterbanks into decibel(dB)features-based spectrograms without applying the Discrete Cosine Transform(DCT).A new dataset was created with converted spectrogram into a 2-D array.Several learning algorithms were implementedwith a 10-fold cross-validationmethod to detect the speaker.The highest accuracy of 90.2%was achieved using Multi-layer Perceptron(MLP)with tanh activation function.The most important output of this study is the inclusion of human voice as a new feature set.展开更多
提出了一种基于梅尔频率倒谱系数(Mel frequency cepstrum coefficients,MFCC)的声音检测装置及算法实现。通过采集声音的波形,结合特征提取和分类算法,实现对不同声音的智能判断。从嵌入式系统硬件设计、声音波形特征提取、声音分类算...提出了一种基于梅尔频率倒谱系数(Mel frequency cepstrum coefficients,MFCC)的声音检测装置及算法实现。通过采集声音的波形,结合特征提取和分类算法,实现对不同声音的智能判断。从嵌入式系统硬件设计、声音波形特征提取、声音分类算法等方面进行了详细的研究,并对实验结果进行了分析。结果表明,该设计方案在声音检测方面具有较高的准确性和可行性。展开更多
针对分布式光纤传感信号噪声强、识别难的问题,本文提出一种基于优化变分模态分解(Variational Mode Decomposition,VMD)融合梅尔频率倒谱系数(Mel Frequency Cepstral Coefficient,MFCC)特征的海缆裸露状态识别方法,用于识别海上风机...针对分布式光纤传感信号噪声强、识别难的问题,本文提出一种基于优化变分模态分解(Variational Mode Decomposition,VMD)融合梅尔频率倒谱系数(Mel Frequency Cepstral Coefficient,MFCC)特征的海缆裸露状态识别方法,用于识别海上风机海底电缆接入端浅埋和裸露两种状态。首先,利用参数优化的VMD对光纤振动信号进行分解,并利用相关系数法筛选本征模态分量(Intrinsic Mode Function,IMF);其次,融合梅尔频率倒谱系数、原始振动信号和所选IMF的时域和频域特征,以及IMF的能量和熵特征构建高维特征集,利用补偿距离评估技术(Compensation Distance Evaluation Technique,CDET)进行降维;最后,设计长短时记忆网络(Long Short Term Memory Network,LSTM)结构,将训练集输入网络进行训练,测试集验证网络的有效性,实现海缆裸露状态识别。通过现场采集的海缆振动数据进行验证,测试准确率达到100%,结果表明该方法能够准确识别和预测海缆裸露状态。展开更多
基金This work was supported by the GRRC program of Gyeonggi province.[GRRC-Gachon2020(B04),Development of AI-based Healthcare Devices].
文摘Automatic speaker recognition(ASR)systems are the field of Human-machine interaction and scientists have been using feature extraction and feature matching methods to analyze and synthesize these signals.One of the most commonly used methods for feature extraction is Mel Frequency Cepstral Coefficients(MFCCs).Recent researches show that MFCCs are successful in processing the voice signal with high accuracies.MFCCs represents a sequence of voice signal-specific features.This experimental analysis is proposed to distinguish Turkish speakers by extracting the MFCCs from the speech recordings.Since the human perception of sound is not linear,after the filterbank step in theMFCC method,we converted the obtained log filterbanks into decibel(dB)features-based spectrograms without applying the Discrete Cosine Transform(DCT).A new dataset was created with converted spectrogram into a 2-D array.Several learning algorithms were implementedwith a 10-fold cross-validationmethod to detect the speaker.The highest accuracy of 90.2%was achieved using Multi-layer Perceptron(MLP)with tanh activation function.The most important output of this study is the inclusion of human voice as a new feature set.
文摘提出了一种基于梅尔频率倒谱系数(Mel frequency cepstrum coefficients,MFCC)的声音检测装置及算法实现。通过采集声音的波形,结合特征提取和分类算法,实现对不同声音的智能判断。从嵌入式系统硬件设计、声音波形特征提取、声音分类算法等方面进行了详细的研究,并对实验结果进行了分析。结果表明,该设计方案在声音检测方面具有较高的准确性和可行性。
文摘针对分布式光纤传感信号噪声强、识别难的问题,本文提出一种基于优化变分模态分解(Variational Mode Decomposition,VMD)融合梅尔频率倒谱系数(Mel Frequency Cepstral Coefficient,MFCC)特征的海缆裸露状态识别方法,用于识别海上风机海底电缆接入端浅埋和裸露两种状态。首先,利用参数优化的VMD对光纤振动信号进行分解,并利用相关系数法筛选本征模态分量(Intrinsic Mode Function,IMF);其次,融合梅尔频率倒谱系数、原始振动信号和所选IMF的时域和频域特征,以及IMF的能量和熵特征构建高维特征集,利用补偿距离评估技术(Compensation Distance Evaluation Technique,CDET)进行降维;最后,设计长短时记忆网络(Long Short Term Memory Network,LSTM)结构,将训练集输入网络进行训练,测试集验证网络的有效性,实现海缆裸露状态识别。通过现场采集的海缆振动数据进行验证,测试准确率达到100%,结果表明该方法能够准确识别和预测海缆裸露状态。
